KlausRobert Müller et al. Big Data and Machine Learning


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1 KlausRobert Müller et al. Big Data and Machine Learning
2 Some Remarks Machine Learning small data (expensive!) big data big data in neuroscience: BCI et al. social media data physics & materials
3 Toward Brain Computer Interfacing KlausRobert Müller, Siamac Fazli, Jan Mehnert, Stefan Haufe, Frank Meinecke, Paul von Bünau, Franz Kiraly, Felix Biessmann, Sven Dähne, Johannes Höhne, Michael Tangermann, Carmen Vidaure, Gabriel Curio, Benjamin Blankertz et al.
4 Invasive BCI at it s best Remark: 24*1000* 3600*30000 ~ 2tb/day [From Schwartz]
5 Noninvasive BrainComputer Interface DECODING
6 BCI for communcation
7 Brain Pong with BBCI Remark: 3*100* 3600*1000 ~ 12Gb/Experiment
8 BBCI paradigms Leitmotiv: let the machines learn  healthy subjects untrained for BCI A: training <10min: right/left hand imagined movements infer the respective brain acivities (ML & SP) B: online feedback session
9 Machine learning approach to BCI: infer prototypical pattern Inference by CSP Algorithm
10 The cerebral cocktail party problem use ICA/NGCA projections for artifact and noise removal feature extraction and selection [cf. Ziehe et al. 2000, Blanchard et al. 2006]
11 BBCI Setup Artifact removal [cf. Müller et al. 2001, 2007, 2008, Dornhege et al. 2003, 2007, Blankertz et al. 2004, 2005, 2006, 2007, 2008]
12 Shifting distributions within experiment
13 20 Correlating apples and oranges [Biessmann et al. Neuroimage 2012, Machine Learning 2010]
14
15 Temporal Dynamics of Web Data
16 Motivation [Biessmann et al, 2012, and submitted]
17 Canonical Trend Analysis for Social Networks
18 Data Extraction
19 Data Extraction: Retweet Location
20 Mean Location of Reweeted News Articles
21 Downsampling of Geographic Information
22 Canonical Trend Model
23 Why projecting on canonical subspace Recent development: tkcca allows to optimally and nonlinearly correlate over time [Biessmann et al 2010]
24 Canonical Trend Analysis
25 Canonical Trend Analysis
26 Efficient Computation of Canonical Trends [Schölkopf, Smola & Müller 98, Boser, Gyon, Vapnik, 92]
27 Efficient Computation of Canonical Trends
28 Efficient Computation of Canonical Trends
29 Comparisons: Mean, PCA and Canonical Trends
30 Comparisons: Mean, PCA and Canonical Trends
31 Comparisons: Mean, PCA and Canonical Trends
32 Comparisons: Mean, PCA and Canonical Trends
33 Canonical Convolution
34 Spatiotemporal Analysis of Retweets of News
35 53 And now for something completely different [Montavon et al 13, Rupp et al 2012.]
36 IPAM 2011 KlausRobert Müller, Matthias Rupp Anatole von Lilienfeld and Alexandre Tkachenko et al
37 Machine Learning for chemical compound space Ansatz: instead of [from von Lilienfeld]
38 Machine Learning for chemical compound space Ansatz: Provide same information to ML as to SE: XYZfile cast data similarly as in the SE: Unique and continuous in all of CCS Translationally, rotationally, permutationally invariant Symmetrical atoms contribute equally ``Coulomb'' Matrix [energy] fill up with zeros for smaller molecules diagonalize OR sort rows according to their norm measure distance between molecules: [from von Lilienfeld]
39 Coulomb representation of molecules M = 2.4 ii Z i M ij = R Z i i Z j R j M {Z 2, R 2 } {Z 1, R 1 } { Z 3, R 3 } {Z 4, R 4 }... M ij + phantom atoms {0,R 21 } {0,R 22 } {0,R 23 } Coulomb Matrix (Rupp12)
40 Kernel ridge regression Distances between M define Gaussian kernel matrix K Predict energy as sum over weighted Gaussians using weights that minimize error in training set Exact solution As many parameters as molecules + 2 global parameters, characteristic lengthscale or kt of system (σ), and noiselevel (λ) [from von Lilienfeld]
41 The data GDB13 database of all organic molecules (within stability & synthetic constraints) of 13 heavy atoms or less: 0.9B compounds Blum & Reymond, JACS (2009) [from von Lilienfeld]
42 Results March 2012 Rupp et al., PRL 9.99 kcal/mol (kernels + eigenspectrum) December 2012 Montavon et al., NIPS 3.51 kcal/mol (deep Neural nets + Coulomb sets) More fun is yet to come... Prediction considered chemically accurate when MAE is below 1 kcal/mol Dataset available at
43 Conclusion Machine Learning is a versatile and ready to use tool for data analysis small data vs. big data fields of ML & Data Bases will hit a limit in near future time for a new marriage
44
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